Rank-density-based multiobjective genetic algorithm and benchmark test function study

Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accu...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 7; no. 4; pp. 325 - 343
Main Authors:	Haiming Lu, Yen, G.G.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.08.2003 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Benchmark testing Computer science; control theory; systems Density Design engineering Distributed computing Evolutionary algorithms Evolutionary computation Exact sciences and technology Genetic algorithms Iron Mathematical analysis Mathematical foundations Mathematical models Mathematics Optimization Pareto optimality Pareto optimization Probability and statistics Ranking Sciences and techniques of general use Statistics Theoretical computing Statistical analysis Pareto optimum Genetic algorithm Evolutionary algorithm Benchmark calculation Multiobjective programming Pareto optimality Multiobjective evolutionary algorithm (MOEA) Optimization Search algorithm multiobjective optimization (MO)
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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